aer-identification

name	aer-identification
description	Use when selecting, implementing, or stress-testing the causal identification strategy for an empirical economics manuscript — difference-in-differences (including staggered designs), instrumental variables (including weak-IV-robust inference), regression discontinuity, synthetic control, or shift-share / Bartik. Apply before writing the introduction or results.

AER Identification

Overview

In modern AER-track empirical economics, identification is the paper. A weak design cannot be rescued by clever writing, more controls, or a larger sample. This skill walks through the five canonical design-based strategies, the modern defaults that have replaced naive textbook implementations, and the referee-anticipating tests each demands.

If the identification strategy is fragile, return to aer-topic-selection. There is no point polishing an indefensible empirical strategy.

When to Use

• Designing the empirical strategy for a new project
• The current strategy is TWFE / first-stage F / naive RDD and the referee will flag it
• A prior submission was rejected on identification grounds and the design needs rebuilding
• Choosing between two candidate identification strategies for the same question

Master Decision Tree

Is treatment assignment plausibly random conditional on observables?
├── Yes, by design (RCT, lottery) → run the RCT analysis; register PAP via AEA RCT Registry
└── No → identification must come from variation
    ├── Sharp threshold in a running variable → RDD (sharp or fuzzy)
    ├── Discrete policy change in some units, not others, over time → DiD
    │     ├── Single treatment date → canonical 2×2 DiD
    │     └── Staggered adoption → Callaway-Sant'Anna or Borusyak-Jaravel-Spiess
    ├── Endogenous regressor + plausibly exogenous shifter → IV
    │     ├── Shifter × pre-existing exposure shares → shift-share / Bartik
    │     └── Single instrument → weak-IV-robust inference if F < 50
    ├── One treated unit / aggregate intervention → synthetic control
    └── None of the above → reconsider the question

Difference-in-Differences

Canonical 2×2 (single treatment date, two groups)

Use TWFE if and only if:

• Treatment timing is simultaneous for all treated units
• The control group is never treated
• Treatment-effect heterogeneity is implausible

Otherwise, TWFE produces biased and often sign-flipped estimates.

Staggered Adoption (most modern applications)

Do not use TWFE. Use one of:

• Callaway and Sant'Anna (2021) — csdid (Stata), did (R). Identifies group-time average treatment effects (ATT(g,t)); estimands are doubly robust; supports event-study aggregation.
• Borusyak, Jaravel, and Spiess (2024) — imputation estimator.
• de Chaisemartin and D'Haultfœuille (2020) — did_multiplegt.
• Sun and Abraham (2021) — interaction-weighted estimator for event studies.

Required diagnostics:

1. Goodman-Bacon decomposition to show the share of weight from "forbidden" comparisons under TWFE
2. Event-study plot with the imputation or Callaway-Sant'Anna estimator
3. Pre-trends test reported as the joint test, not just the visual
4. Heterogeneity by treatment cohort

Pre-Trends

A flat pre-trend is necessary but not sufficient. Report:

• Visual event-study plot with 95% confidence intervals
• Formal joint test of pre-period coefficients (p-value)
• Honest DiD (Rambachan-Roth 2023) sensitivity bounds for the post-period

Instrumental Variables

Weak Instruments

The first-stage F > 10 rule is obsolete. Modern conventions:

• For just-identified models: report Anderson-Rubin (AR) confidence sets as the primary inference. AR has correct size regardless of instrument strength.
• For F < 50: 2SLS confidence intervals are unreliable; AR is required, not optional.
• Stock-Yogo critical values for TSLS bias assume homoskedasticity and are rarely valid in modern clustered settings.

Use weakivtest (Stata), ivDiag (R), or the Olea-Pflueger effective F statistic.

Exclusion Restriction

The IV's credibility depends on a story, not a test. State the exclusion restriction in one sentence in the introduction and defend it with:

• Institutional narrative (one paragraph)
• A placebo regression where the instrument predicts an outcome it should not affect
• Sensitivity analysis: how much exclusion-restriction violation would overturn the result (Conley et al. 2012)

Shift-Share / Bartik

Two valid sources of identification, with very different implications:

1. Exogenous shares (Goldsmith-Pinkham, Sorkin, Swift 2020) — argue that pre-existing exposure shares are conditionally exogenous; report the Rotemberg weights and inspect the top-5 industries driving identification.
2. Exogenous shocks (Borusyak, Hull, Jaravel 2022; Adão, Kolesár, Morales 2019) — argue that aggregate shocks are as-good-as-random; report shock-level inference.

Pick one explicitly. Do not hand-wave between the two.

Regression Discontinuity

Modern Defaults

• Local linear regression with a triangular kernel. Polynomials of order > 1 are discouraged (Gelman-Imbens 2019).
• MSE-optimal bandwidth (Calonico-Cattaneo-Titiunik 2014) with the robust bias-corrected confidence interval. Use rdrobust.
• Donut RDD if bunching near the cutoff is a concern.
• Covariate adjustment for efficiency; main result must hold without it.

Required Diagnostics

1. McCrary (2008) / Cattaneo-Jansson-Ma (2020) density test for manipulation of the running variable
2. Balance tests on predetermined covariates at the cutoff
3. Placebo cutoffs away from the true threshold
4. Bandwidth sensitivity — show the estimate across at least three bandwidths
5. Visual RD plot using rdplot with the binning method explicitly stated

Synthetic Control

When Appropriate

• One (or few) treated units
• Long pre-treatment outcome series (≥ 10 periods)
• A large donor pool of plausibly comparable untreated units
• Aggregate intervention (policy at the country, state, city level)

Modern Extensions

• Generalized synthetic control (Xu 2017) for multiple treated units
• Augmented synthetic control (Ben-Michael, Feller, Rothstein 2021) for bias correction
• Synthetic DiD (Arkhangelsky et al. 2021) combining SCM and DiD weighting

Required Diagnostics

1. Placebo (in-time): apply SCM to pre-treatment fake intervention dates
2. Placebo (in-space): apply SCM to every donor as if it were treated; report the distribution of placebo effects
3. Permutation inference / Fisher exact p-value
4. Weight vector reported in the appendix; donors with > 10% weight discussed

Field Experiments and RCTs

If the paper uses a field experiment:

• Register with AEA RCT Registry before the intervention begins. AEA journals require this prior to submission.
• Pre-analysis plan (PAP) posted before unblinding. Per Olken and others, keep the PAP moderate in scope — pre-specify primary outcomes and the analysis specification, leave exploratory work clearly labeled as such.
• Power calculations in the manuscript or appendix.
• Multiple-hypothesis correction if more than one primary outcome.
• Attrition documented and tested for differential attrition by treatment arm.

Mechanism vs. Identification

A common confusion: identification answers whether X causes Y; mechanism answers why. Mechanism evidence should not weaken the identification of the main effect. Run:

• Subgroup heterogeneity (does the effect concentrate where theory predicts?)
• Mediation analysis only if the mediator is itself plausibly exogenous (rare)
• Auxiliary outcomes consistent with the proposed channel

Red Flags for Referees

• TWFE on staggered data with no Goodman-Bacon decomposition
• First-stage F = 12 cited as evidence of instrument strength
• RDD with a polynomial of order 4
• Synthetic control with no placebo inference
• DiD with a "control group" of eventually-treated units
• IV exclusion restriction defended only by "we control for X"
• Quoting an Angrist-Pischke citation as a substitute for showing the diagnostic

Repository Resources

When working from the AER-skills repository or plugin bundle, load only the relevant resource:

• Staggered DiD implementation: templates/stata/03_main_did.do, templates/r/03_main_did.R, or templates/python/main_did.py
• Classic design examples: examples/aer-exemplars.md

Handoff

STRATEGY: <DiD | IV | RDD | SCM | shift-share | RCT>
MODERN ESTIMATOR USED: <yes / no / which>
REQUIRED DIAGNOSTICS REPORTED: <list>
INFERENCE METHOD: <robust / cluster-robust / AR / wild bootstrap / permutation>
WEAK-IV / TWFE / POLY-ORDER RED FLAGS: <list or "none">
NEXT SKILL: aer-robustness

Anti-Patterns

• Defending an old design ("the prior literature used TWFE") when modern estimators exist
• Reporting OLS-with-controls as the main specification and IV/RD as "robustness"
• Using more than one identification strategy as if they were independent confirmations when they share identifying variation
• Footnoting the identifying assumption instead of stating it in the introduction